Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network

• URL: http://arxiv.org/abs/2009.14563v1
• Date: Wed, 30 Sep 2020 11:06:38 GMT
• Title: Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network
• Authors: Sijin Kim, Namhyuk Ahn, Kyung-Ah Sohn
• Abstract summary: We introduce a spatially-heterogeneous distortion dataset in which multiple corruptions are applied to the different locations of each image. Motivated by the multi-task learning, we design our network to have multiple paths that learn both common and distortion-specific representations. Our model is effective for restoring real-world distortions and we experimentally verify that our method outperforms other models designed to manage both single distortion and multiple distortions.
• Score: 11.048041466120589
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, deep learning-based methods have been successfully applied to the image distortion restoration tasks. However, scenarios that assume a single distortion only may not be suitable for many real-world applications. To deal with such cases, some studies have proposed sequentially combined distortions datasets. Viewing in a different point of combining, we introduce a spatially-heterogeneous distortion dataset in which multiple corruptions are applied to the different locations of each image. In addition, we also propose a mixture of experts network to effectively restore a multi-distortion image. Motivated by the multi-task learning, we design our network to have multiple paths that learn both common and distortion-specific representations. Our model is effective for restoring real-world distortions and we experimentally verify that our method outperforms other models designed to manage both single distortion and multiple distortions.

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